Genome-Wide Temporal Profiling of Transcriptome and Open Chromatin of Early Cardiomyocyte Differentiation Derived From hiPSCs and hESCs

Abstract

Rationale Recent advances have improved our ability to generate cardiomyocytes from human induced pluripotent stem cells (hiPSCs) and human embryonic stem cells (hESCs). However, our understanding of the transcriptional regulatory networks underlying early stages (i.e. from mesoderm to cardiac mesoderm) of cardiomyocyte differentiation remains limited. Objective To characterize transcriptome and chromatin accessibility during early cardiomyocyte differentiation from hiPSCs and hESCs. Methods and Results We profiled the temporal changes in transcriptome and chromatin accessibility at genome-wide levels during cardiomyocyte differentiation derived from two hiPSC lines and two hESC lines at four stages: pluripotent stem cells, mesoderm, cardiac mesoderm, and differentiated cardiomyocytes. Overall, RNA-seq analysis revealed that transcriptomes during early cardiomyocyte differentiation were highly concordant between hiPSCs and hESCs, and clustering of four cell lines within each time-point demonstrated that changes in genome-wide chromatin accessibility were similar across hiPSC and hESC cell lines. Weighted gene co-expression network analysis (WGCNA) identified several modules that were strongly correlated with different stages of cardiomyocyte differentiation. Several novel genes were identified with high weighted-connectivity within modules and exhibited co-expression patterns with other genes, including non-coding RNA LINC01124 and uncharacterized RNA AK127400 in the module related to the mesoderm stage; and ZEB1 in the module correlated with post-cardiac mesoderm. We further demonstrated that ZEB1 is required for early cardiomyocyte differentiation. In addition, based on integrative analysis of both WCGNA and TF-motif enrichment analysis, we determined numerous TFs likely to play important roles at different stages during cardiomyocyte differentiation, such as T and EOMES (mesoderm); LEF1 and MESP1 (from mesoderm to cardiac mesoderm); MEIS1 and GATA4 (post-cardiac mesoderm); JUN and FOS families, and MEIS2 (cardiomyocyte). Conclusions Both hiPSCs and hESCs share similar transcriptional regulatory mechanisms underlying early cardiac differentiation, and our results have revealed transcriptional regulatory networks and new factors (e.g. ZEB1) controlling early stages of cardiomyocyte differentiation.

Dynamic reorganization of nuclear architecture during human cardiogenesis

“…Stage-specific peaks are enriched in motifs corresponding to developmental transcription factors ( fig. S6E), in agreement with another recent study (20). Interrogating the regions that transition from B to A, we find that these regions are enriched for peaks present in CP and CM stages and significantly depleted in constitutive peaks (Fig.…”

Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

“…However, the majority of studies focused on differentially expressed genes and ignored the high relationship between genes. At present, WGCNA plays a significant role in multiple fields, such as cancer, nervous system and genetic data analysis, which is extremely useful for identifying potential candidate biomarkers or novel treatment targets …”

“…At present, WGCNA plays a significant role in multiple fields, such as cancer, nervous system and genetic data analysis, which is extremely useful for identifying potential candidate biomarkers or novel treatment targets. [17][18][19][20] Our study utilized the data related to breast cancer from The Cancer Genomes Atlas (TCGA) database to identify novel lncRNAs.…”

Profiling of pluripotency factors in individual stem cells and early embryos

“…However, compared to the fraction of high input ATAC-seq peaks (Q. Liu et al, 2017) identified by single cell ATAC-seq (Buenrostro et al, 2015b), single cell uliCUT&RUN libraries identified "gold standard" TF binding sites at a higher rate on average ( Figure S3), demonstrating uncommonly high sensitivity of single cell profiling using the uliCUT&RUN method. For both methods, the incomplete identification of high cell number peaks likely reflects both the technical challenge of recovering every fragment of DNA released from a single nucleus and meaningful biological variation.…”